{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import sklearn\n",
    "import xgboost as xgb\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier,GradientBoostingClassifier,ExtraTreesClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import KFold, RepeatedKFold;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load in train and test datasets\n",
    "train = pd.read_csv(\"./data/train.csv\")\n",
    "test = pd.read_csv(\"./data/test.csv\")\n",
    "\n",
    "#Store our passenger ID for easy access\n",
    "PassengerId = test['PassengerId']\n",
    "\n",
    "train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data = [train, test]\n",
    "\n",
    "train['Name_length'] = train['Name'].apply(len)\n",
    "test['Name_length'] = test['Name'].apply(len)\n",
    "\n",
    "train['Has_Cabin'] = train['Cabin'].apply(lambda x:0 if type(x) == float else 1)\n",
    "test['Has_Cabin'] = test['Cabin'].apply(lambda x:0 if type(x) == float else 1)\n",
    "\n",
    "#Feature engineering\n",
    "#Create new feature FamilySize as a combination of sibsp and Parch\n",
    "for dataset in full_data:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "# Create new feature IsAlone from FamilySize\n",
    "for dataset in full_data:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone' ] = 1\n",
    "\n",
    "for dataset in full_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna('S')\n",
    "for dataset in full_data:\n",
    "    dataset['Fare']= dataset['Fare'].fillna(train['Fare'].median())\n",
    "train['CategoricalFare'] = pd.qcut(train['Fare'],4)\n",
    "\n",
    "for dataset in full_data:\n",
    "    age_avg = dataset['Age'].mean()\n",
    "    age_std = dataset['Age'].std()\n",
    "    age_null_count = dataset['Age'].isnull().sum()\n",
    "    age_null_random_list = np.random.randint(age_avg - age_std, age_avg + age_std, size=age_null_count)\n",
    "    dataset['Age'][np.isnan(dataset['Age'])] = age_null_random_list\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "train['CategoricalAge'] = pd.cut(train['Age'], 5)\n",
    "# Define the function to extract titles from passengers' names \n",
    "def get_title(name):\n",
    "    title_search = re.search(' ([A-Za-z]+)\\.', name)\n",
    "    if title_search:\n",
    "        return title_search.group(1)\n",
    "    return \"\"\n",
    "# Create a new feature Title, containing the titles of passenger names\n",
    "for dataset in full_data:\n",
    "    dataset['Title'] = dataset['Name'].apply(get_title)\n",
    "    \n",
    "# Group all non-common titles into one single grouping \"Rare\"\n",
    "for dataset in full_data:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady','Countess','Capt','Col','Don',\n",
    "                                                 'Dr','Major','Rev','Sir','Jonkheer','Dona'],'Rare')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "\n",
    "for dataset in full_data:\n",
    "    dataset['Sex'] = dataset['Sex'].map( {'female':0, 'male':1}).astype(int)\n",
    "    title_mapping = {\"Mr\":1 ,\"Miss\":2, \"Mrs\":3 ,\"Master\": 4 ,\"Rare\":5}\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "    \n",
    "    dataset['Embarked'] = dataset['Embarked'].map({'S':0, 'C':1 ,'Q':2}).astype(int)\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare']  = 0\n",
    "    dataset.loc[ (dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[ (dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare'] = 2\n",
    "    dataset.loc[ (dataset['Fare'] > 31), 'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "    \n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age' ] = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'Age'] = 4 ;\n",
    "    \n",
    "# Feature selection\n",
    "drop_elements = ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp']\n",
    "train = train.drop(drop_elements, axis = 1)\n",
    "train = train.drop(['CategoricalAge', 'CategoricalFare'], axis = 1)\n",
    "test  = test.drop(drop_elements, axis = 1)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Name_length</th>\n",
       "      <th>Has_Cabin</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Parch  Fare  Embarked  Name_length  Has_Cabin  \\\n",
       "0         0       3    1    1      0     0         0           23          0   \n",
       "1         1       1    0    2      0     3         1           51          1   \n",
       "2         1       3    0    1      0     1         0           22          0   \n",
       "3         1       1    0    2      0     3         0           44          1   \n",
       "4         0       3    1    2      0     1         0           24          0   \n",
       "\n",
       "   FamilySize  IsAlone  Title  \n",
       "0           2        0      1  \n",
       "1           2        0      3  \n",
       "2           1        1      2  \n",
       "3           2        0      3  \n",
       "4           1        1      1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ccce8df98>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAy8AAAL5CAYAAAC5PRu2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nogkZvERERERERBP+D7UJw6DWzM8PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ccce8d5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormap = plt.cm.RdBu\n",
    "plt.figure(figsize=(14,12))\n",
    "plt.title('Pearson Correlation of Features', y=1.05, size=15)\n",
    "sns.heatmap(train.astype(float).corr(),linewidths=0.1,vmax=1.0, \n",
    "            square=True, cmap=colormap, linecolor='white', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SklearnHelper(object):\n",
    "    def __init__(self, clf, seed=0, params=None):\n",
    "        params['random_state'] = seed\n",
    "        self.clf = clf(**params)\n",
    "        \n",
    "    def train(self, x_train, y_train):\n",
    "        self.clf.fit(x_train, y_train)\n",
    "    \n",
    "    def predict(self, x):\n",
    "        return self.clf.predict(x)\n",
    "    \n",
    "    def fit(self, x, y):\n",
    "        return self.clf.fit(x,y)\n",
    "    \n",
    "    def feature_importances(self, x, y):\n",
    "        print(self.clf.fit(x,y).feature_importances_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ntrain = train.shape[0]\n",
    "ntest = test.shape[0]\n",
    "SEED = 0\n",
    "NFOLDS = 5\n",
    "kf = KFold(n_splits=NFOLDS, shuffle=False ,random_state=SEED) #K-交叉验证\n",
    "\n",
    "#kf = RepeatedKFold(n_splits=NFOLDS, n_repeats =2 ,random_state=SEED) #K-交叉重复验证\n",
    "def get_oof(clf, x_train, y_train, x_test):   #x_train 训练集 y_train 训练集标签 x_test测试集\n",
    "    oof_train = np.zeros((ntrain,))\n",
    "    oof_test = np.zeros((ntest,))\n",
    "    oof_test_skf = np.empty((NFOLDS, ntest))\n",
    "    \n",
    "    for i,(train_index, test_index) in enumerate(kf.split(x_train)): #train_index 被分到训练集中的index  同理test_index\n",
    "        x_tr = x_train[train_index]\n",
    "        y_tr = y_train[train_index]\n",
    "        x_te = x_train[test_index]\n",
    "        y_te = y_train[test_index]\n",
    "        clf.train(x_tr, y_tr)\n",
    "        \n",
    "        oof_train[test_index] = clf.predict(x_te) # 用验证数据集验证训练集训练好的模型\n",
    "        oof_test_skf[i, :] = clf.predict(x_test)  # 用测试集测试\n",
    "        print(\"K-fold: %d accuracy score is %f \" % (i,accuracy_score(y_te.reshape(-1),clf.predict(x_te).reshape(-1))))\n",
    "    oof_test[:] = oof_test_skf.mean(axis=0)\n",
    "    return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put in our parameters for said classifiers\n",
    "# Forest note:\n",
    "# the main parameters for these \"forest\" algorithm is 'n_estimators' and 'max_features'\n",
    "# Random Forest parameters\n",
    "rf_params = {\n",
    "    'n_jobs': -1,                # the number of jobs to run in parallel for both fit and predict\n",
    "    'n_estimators': 1200,         # The number of trees in the forest\n",
    "     #'warm_start': True, \n",
    "     #'max_features': 0.2,\n",
    "    'max_depth': 20,\n",
    "    'min_samples_leaf': 2,\n",
    "    'max_features' : 'sqrt',    # the max number of features to consider when looking for the best split\n",
    "    'verbose': 0\n",
    "}\n",
    "\n",
    "# Extra Trees Parameters\n",
    "et_params = {\n",
    "    'n_jobs': -1,\n",
    "    'n_estimators':1200,\n",
    "    #'warm_start': True,\n",
    "    #'max_features': 0.5,\n",
    "    'max_depth': 20,\n",
    "    'min_samples_leaf': 2,\n",
    "    'verbose': 0\n",
    "}\n",
    "\n",
    "# AdaBoost parameters\n",
    "ada_params = {\n",
    "    'n_estimators': 1200,\n",
    "    'learning_rate' : 0.1\n",
    "}\n",
    "\n",
    "# Gradient Boosting parameters\n",
    "gb_params = {\n",
    "    'n_estimators': 900,\n",
    "     #'max_features': 0.2,\n",
    "    'max_depth': 15,\n",
    "    'min_samples_leaf': 3,\n",
    "    'verbose': 0\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Support Vector Classifier parameters \n",
    "svc_params = {\n",
    "    'kernel' : 'poly',\n",
    "    'C' : 0.0453\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 5 objects that represent our 4 models\n",
    "rf = SklearnHelper(clf=RandomForestClassifier, seed=SEED, params=rf_params)\n",
    "et = SklearnHelper(clf=ExtraTreesClassifier, seed=SEED, params=et_params)\n",
    "ada = SklearnHelper(clf=AdaBoostClassifier, seed=SEED, params=ada_params)\n",
    "gb = SklearnHelper(clf=GradientBoostingClassifier, seed=SEED, params=gb_params)\n",
    "svc = SklearnHelper(clf=SVC, seed=SEED, params=svc_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Numpy arrays of train, test and target ( Survived) dataframes to feed into our models\n",
    "y_train = train['Survived'].ravel()\n",
    "train = train.drop(['Survived'], axis=1)\n",
    "x_train = train.values # Creates an array of the train data\n",
    "x_test = test.values # Creats an array of the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-fold: 0 accuracy score is 0.804469 \n",
      "K-fold: 1 accuracy score is 0.814607 \n",
      "K-fold: 2 accuracy score is 0.837079 \n",
      "K-fold: 3 accuracy score is 0.780899 \n",
      "K-fold: 4 accuracy score is 0.876404 \n",
      "K-fold: 0 accuracy score is 0.804469 \n",
      "K-fold: 1 accuracy score is 0.808989 \n",
      "K-fold: 2 accuracy score is 0.837079 \n",
      "K-fold: 3 accuracy score is 0.780899 \n",
      "K-fold: 4 accuracy score is 0.870787 \n",
      "K-fold: 0 accuracy score is 0.832402 \n",
      "K-fold: 1 accuracy score is 0.814607 \n",
      "K-fold: 2 accuracy score is 0.803371 \n",
      "K-fold: 3 accuracy score is 0.792135 \n",
      "K-fold: 4 accuracy score is 0.870787 \n",
      "K-fold: 0 accuracy score is 0.787709 \n",
      "K-fold: 1 accuracy score is 0.786517 \n",
      "K-fold: 2 accuracy score is 0.825843 \n",
      "K-fold: 3 accuracy score is 0.803371 \n",
      "K-fold: 4 accuracy score is 0.848315 \n",
      "K-fold: 0 accuracy score is 0.832402 \n",
      "K-fold: 1 accuracy score is 0.825843 \n",
      "K-fold: 2 accuracy score is 0.803371 \n",
      "K-fold: 3 accuracy score is 0.775281 \n",
      "K-fold: 4 accuracy score is 0.853933 \n",
      "Training is complete\n"
     ]
    }
   ],
   "source": [
    "# Create our OOF train and test predictions. These base results will be used as new features\n",
    "et_oof_train, et_oof_test = get_oof(et, x_train, y_train, x_test) # Extra Trees\n",
    "rf_oof_train, rf_oof_test = get_oof(rf,x_train, y_train, x_test) # Random Forest\n",
    "ada_oof_train, ada_oof_test = get_oof(ada, x_train, y_train, x_test) # AdaBoost \n",
    "gb_oof_train, gb_oof_test = get_oof(gb,x_train, y_train, x_test) # Gradient Boost\n",
    "svc_oof_train, svc_oof_test = get_oof(svc,x_train, y_train, x_test) # Support Vector Classifier\n",
    "\n",
    "print(\"Training is complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rf:  0.894501\n",
      "et:  0.893378\n",
      "ada: 0.842873\n",
      "gb:  0.940516\n",
      "svc: 0.833895\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "#print(rf_oof_train.reshape(-1))\n",
    "#print(accuracy_score(y_train,rf_oof_train.reshape(-1)))\n",
    "print(\"rf:  %f\" % (accuracy_score(y_train,rf.predict(x_train))))\n",
    "print(\"et:  %f\" % (accuracy_score(y_train,et.predict(x_train))))\n",
    "print(\"ada: %f\" % (accuracy_score(y_train,ada.predict(x_train))))\n",
    "print(\"gb:  %f\" % accuracy_score(y_train,gb.predict(x_train)))\n",
    "print(\"svc: %f\" % accuracy_score(y_train,svc.predict(x_train)))\n",
    "# print(rf.predict(x_train))\n",
    "# print(accuracy_score(y_train,et_oof_train.reshape(-1)))\n",
    "# print(accuracy_score(y_train,ada_oof_train.reshape(-1)))\n",
    "# print(accuracy_score(y_train,gb_oof_train.reshape(-1)))\n",
    "# print(accuracy_score(y_train,svc_oof_train.reshape(-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.892255892256\n"
     ]
    }
   ],
   "source": [
    "train['train_predict']=rf.predict(x_train)+et.predict(x_train)+ada.predict(x_train)+gb.predict(x_train)+svc.predict(x_train)\n",
    "train['train_predict']=train['train_predict'].astype('int')\n",
    "#print(train.head(10))\n",
    "\n",
    "train.loc[ train['train_predict'] < 3, 'train_predict' ] = 0\n",
    "train.loc[ train['train_predict'] >= 3, 'train_predict' ] = 1\n",
    "train.head(10)\n",
    "print(accuracy_score(y_train,train['train_predict'].ravel()))\n",
    "#直接某个基学习器\n",
    "#print(rf_oof_test.round())\n",
    "# StackingSubmission = pd.DataFrame({ 'PassengerId': PassengerId,\n",
    "#                              'Survived': gb.predict(x_test)})\n",
    "# StackingSubmission.to_csv(\"GBSubmission.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 4 0 0 3 0 5 0 5 0 0 0 5 0 5 5 0 0 2 5 0 2 5 1 5 0 5 0 1 0 0 0 2 5 4 4 2\n",
      " 3 0 1 0 0 0 4 5 0 2 0 5 5 1 0 5 4 1 0 0 0 0 5 0 0 0 4 5 5 5 0 1 4 5 0 2 1\n",
      " 5 2 0 5 0 5 4 0 0 0 0 0 4 2 5 5 1 0 4 0 0 0 5 0 4 0 5 0 0 0 5 0 0 0 0 0 0\n",
      " 5 5 5 5 0 0 5 0 5 5 0 5 0 0 3 0 5 1 0 1 2 0 0 0 0 1 0 3 0 0 5 0 0 5 0 1 0\n",
      " 1 1 5 0 0 4 1 0 5 5 1 4 4 5 5 0 0 4 1 0 4 2 0 0 1 1 0 5 5 2 5 5 0 4 4 0 5\n",
      " 0 5 0 0 0 0 0 5 0 5 0 5 3 0 2 4 5 4 5 0 1 5 0 5 0 0 0 0 5 0 0 5 0 5 0 5 0\n",
      " 5 0 5 5 0 5 0 0 0 5 0 0 3 0 1 0 5 5 5 5 4 0 4 1 4 0 4 5 5 0 4 0 0 0 0 0 5\n",
      " 0 0 0 5 5 1 0 1 0 2 0 1 0 5 5 0 5 0 0 0 0 5 4 5 5 5 0 0 0 0 0 0 5 0 3 0 0\n",
      " 5 0 0 0 0 0 0 0 5 5 1 5 0 2 0 0 0 5 5 5 1 0 0 0 0 0 0 0 5 0 4 4 0 0 5 1 0\n",
      " 5 0 2 0 0 0 1 0 0 0 5 4 4 0 5 0 5 5 0 0 0 5 0 5 0 0 5 0 5 5 0 5 0 0 3 5 1\n",
      " 0 5 0 0 5 5 1 0 0 0 0 0 4 2 0 5 0 0 0 0 0 5 5 1 0 5 0 5 0 0 5 0 5 0 2 3 0\n",
      " 0 5 4 5 5 5 1 5 0 0 4]\n",
      "[0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1]\n"
     ]
    }
   ],
   "source": [
    "prediction=rf.predict(x_test)+et.predict(x_test)+ada.predict(x_test)+gb.predict(x_test)+svc.predict(x_test)\n",
    "print(prediction)\n",
    "prediction=[1 if x >=3 else 0 for x in prediction]\n",
    "print(prediction)\n",
    "# Generate Submission File \n",
    "StackingSubmission = pd.DataFrame({ 'PassengerId': PassengerId,\n",
    "                            'Survived': prediction })\n",
    "StackingSubmission.to_csv(\"EnsemleSubmission.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.concatenate(( et_oof_train, rf_oof_train, ada_oof_train, gb_oof_train, svc_oof_train), axis=1)\n",
    "x_test = np.concatenate(( et_oof_test, rf_oof_test, ada_oof_test, gb_oof_test, svc_oof_test), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm = xgb.XGBClassifier(\n",
    "    #learning_rate = 0.02,\n",
    " n_estimators= 1000,\n",
    " max_depth= 5,\n",
    " min_child_weight= 2,\n",
    " #gamma=1,\n",
    " gamma=0.9,                        \n",
    " subsample=0.8,\n",
    " colsample_bytree=0.8,\n",
    " objective= 'binary:logistic',\n",
    " nthread= -1,\n",
    " scale_pos_weight=1).fit(x_train, y_train)\n",
    "predictions = gbm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83950617284\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_train,gbm.predict(x_train).reshape(-1).tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate Submission File \n",
    "StackingSubmission = pd.DataFrame({ 'PassengerId': PassengerId,\n",
    "                            'Survived': predictions })\n",
    "StackingSubmission.to_csv(\"StackingSubmission.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
